Results 91 to 100 of about 4,362,963 (201)
On the behaviour of residual plots in regression [PDF]
, 1995 Properties of least squares versus robust regression residual plots are compared under a common set of ...
Velilla Cerdan, Santiago +1 more
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Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data. [PDF]
Front Genet, 2021 Bi W, Lee S.
europepmc +1 more source
The Application of Robust Regression to a Production Function Comparison – the Example of Swiss Corn [PDF]
The adequate representation of crop response functions is crucial for agri-environmental modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms.
Robert Finger, Werner Hediger
core
Robust estimators for the fixed effects panel data model. [PDF]
The presence of outlying observations in panel data can affect the classical estimates in a dramatic way. Nevertheless the common practice seems to disregard the problem.
Croux, Christophe, Bramati, M
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Strategy-Proof Estimators for Simple Regression [PDF]
In this paper we propose a whole class of estimators (“clockwise repeated median estimators” or CRM) for the simple regression model that are immune to manipulation by the agents generating the data.
Juan Perote Peña, Javier Perote Peña
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Robust methods of building regression models : an application to the housing sector. [PDF]
This article studies robustification strategies for the linear model in the presence of outliers. The advantages of an internal analysis of the robustness of least squares for a given sample are pointed out.
Ruiz-Castillo, Javier, Peña, Daniel
core
Robust online signal extraction from multivariate time series [PDF]
We introduce robust regression-based online filters for multivariate time series and discuss their performance in real time signal extraction settings. We focus on methods that can deal with time series exhibiting patterns such as trends, level changes ...
Gather, Ursula, Lanius, Vivian
core
Robust Learning from Bites [PDF]
Many robust statistical procedures have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets. Secondly, robust confidence intervals for the estimated parameters or robust predictions according to the
Christmann, Andreas
core
Monte Carlo Techniques in Studying Robust Estimators [PDF]
Recent work on robust estimation has led to many procedures, which are easy to formulate and straightforward to program but difficult to study analytically.
David C. Hoaglin
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Robust estimation of dimension reduction space [PDF]
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al.
Wolfgang Härdle, Pavel Cizek
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